Drone-Based Container Crane Inspection: Concept, Challenges and Preliminary Results

Maboudi, Mehdi; Alamouri, Ahmed; López, V. De Arriba; Bajauri, M. S.; Berger, Christian; Gerke, Markus

doi:10.5194/isprs-annals-v-1-2021-121-2021

Cited by 3 publications

(6 citation statements)

References 16 publications

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“…One of the most challenging is the detection of fatigue-related damage, which is often too small to be identified by the human eye or high-resolution cameras (Li 2006). The advancement of UAVs and associated hardware and software has paved the way for their integration into inspection systems for complex structures, including bridges (Chen et al 2019;Zollini et al 2020;Aliyari et al 2021) and container cranes (Stein 2018;Maboudi et al 2021). In the following paragraphs, an overview of the proposed methodologies and approaches to the aforementioned topics is presented.…”

Section: Related Workmentioning

confidence: 99%

“…NB classifier (Rish 2001) uses the a priori probabilities of the events (training samples) to estimate the probability of future events (test set) by means of Bayes' theorem. More detail about this approach is mentioned in our preliminary study (Maboudi et al 2021).…”

Section: Classification Approachesmentioning

confidence: 99%

“…Drones a.k.a. unmanned aerial vehicles (UAVs) are widely used agile platforms for inspection purposes (Nooralishahi et al 2022;Maboudi et al 2021;Moreno-Jacobo et al 2021;Savino and Tondolo 2023). Their integration with various systems enhances sustainability and practical applications, especially in construction and infrastructure.…”

Section: Introductionmentioning

confidence: 99%

“…The focus of our work is on the critical areas of container gantry cranes, where defects are more likely to occur. Through our preliminary study (Maboudi et al 2021), we have demonstrated the overall workflow for health monitoring using UAV images. However, in this paper, we go a step further and present a state-of-the-art approach that addresses the limitations of existing works and provides a more effective and automated workflow.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic non-destructive UAV-based structural health monitoring of steel container cranes

De Arriba López,

Maboudi,

Achanccaray

et al. 2023

Appl Geomat

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Container cranes are of key importance for maritime cargo transportation. The uninterrupted and all-day operation of these container cranes, which directly affects the efficiency of the port, necessitates the continuous inspection of these massive hoisting steel structures. Due to the large size of cranes, the current manual inspections performed by expert climbers are costly, risky, and time-consuming. This motivates further investigations on automated non-destructive approaches for the remote inspection of fatigue-prone parts of cranes. In this paper, we investigate the effectiveness of color space-based and deep learning-based approaches for separating the foreground crane parts from the whole image. Subsequently, three different ML-based algorithms (k-Nearest Neighbors, Random Forest, and Naive Bayes) are employed to detect the rust and repainting areas from detected foreground parts of the crane body. Qualitative and quantitative comparisons of the results of these approaches were conducted. While quantitative evaluation of pixel-based analysis reveals the superiority of the k-Nearest Neighbors algorithm in our experiments, the potential of Random Forest and Naive Bayes for region-based analysis of the defect is highlighted.

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Section: Related Workmentioning

confidence: 99%

Section: Classification Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic non-destructive UAV-based structural health monitoring of steel container cranes

De Arriba López,

Maboudi,

Achanccaray

et al. 2023

Appl Geomat

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“…UAVs are utilized in various monitoring and inspection projects [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. Especially the demand for high-quality 3-D models in many applications such as construction, urban modelling, and emergency rescue is still increasing [13], [14], [15], [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

A Review on Viewpoints and Path Planning for UAV-Based 3-D Reconstruction

Maboudi

Homaei

Song³

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency, and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3-D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This article investigates a wide range of model-free and model-based algorithms for viewpoints and path planning for 3-D reconstruction of large-scale objects. It presents a bibliography of more than 200 references to cover different aspects of the topic. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3-D reconstruction purposes. In addition to discussing the evaluation strategies, this article also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.

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Deep Learning-Based Boolean, Time Series, Error Detection, and Predictive Analysis in Container Crane Operations

Awasthi,

Krpalkova,

Walsh

2024

Algorithms

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Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LSTM model was trained on real operational error data from container cranes. The custom algorithm employs the Synthetic Minority Oversampling TEchnique (SMOTE) to balance the imbalanced data for operational data errors (i.e., too few minority class samples). Python was used to program. Pearson, Spearman, and Kendall correlation matrices and covariance matrices are presented. The model’s training and validation loss is shown, and the remaining data are predicted. The test set (30% of actual data) and forecasted data had RMSEs of 0.065. A heatmap of a confusion matrix was created using Matplotlib and Seaborn. Additionally, the error outputs for the time series for the next n seconds were projected, with the n seconds input by the user. Accuracy was 0.996, precision was 1.00, recall was 0.500, and f1 score was 0.667, according to the evaluation criteria that were produced. Experiments demonstrated that the technique is capable of identifying critical elements. Thus, future attempts will improve the model’s structure to forecast industrial big data errors. However, the advantage is that it can handle imbalanced data, which is usually what most industries have. With additional data, the model can be further improved.

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Drone-Based Container Crane Inspection: Concept, Challenges and Preliminary Results

Cited by 3 publications

References 16 publications

Automatic non-destructive UAV-based structural health monitoring of steel container cranes

Automatic non-destructive UAV-based structural health monitoring of steel container cranes

A Review on Viewpoints and Path Planning for UAV-Based 3-D Reconstruction

Deep Learning-Based Boolean, Time Series, Error Detection, and Predictive Analysis in Container Crane Operations

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